Flexible tree partition and representation for point cloud coding

ABSTRACT

A method of decoding encoded information of a point cloud may be performed by at least one processor and comprises: obtaining an encoded bitstream, the encoded bitstream including encoded information of a point cloud including a set of points in a three-dimensional space; and determining a type of partitioning used to encode the information of the point cloud by at least one of parsing signals of at least three binary syntaxes or inferring at least one syntax of the at least three binary syntaxes.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a Continuation of U.S. application Ser. No.17/085,395, filed Oct. 30, 2020, which is based on and claims priorityunder 35 U.S.C. § 119 to U.S. Provisional Patent Application No.62/972,161, filed Feb. 10, 2020, in the U.S. Patent & Trademark Office,the disclosure of which is incorporated by reference herein in itsentirety.

BACKGROUND

Point cloud modeling has been widely used in recent years. For example,it is used in autonomous driving vehicles for object detection andlocalization, in geographic information systems (GIS) for mapping, andin cultural heritage projects to visualize and archive cultural heritageobjects and collections, etc.

Point clouds contain a set of high dimensional points, typically ofthree dimensions (3D), each including 3D positional information andadditional attributes such as color, reflectance, etc. The highdimensional points can be captured using multiple cameras and depthsensors, via Light Detection and Ranging (LIDAR) in various setups, andmay be made up of thousands to billions of points, thereby allowingrealistic representations of original scenes.

Compression technologies are needed to reduce the amount of datarequired to represent a point cloud for faster transmission and/orstorage reduction. As explained in Non-Patent Literature 1 andNon-Patent Literature 2, the Moving Picture Experts Group (MPEG), aworking group of the International Organization for Standardization(ISO) and the International Electrotechnical Commission (IEC), hascreated a joint technical committee (JTC 1/SC 29/WG 11) and an ad-hocgroup (MPEG-PCC) to standardize compression techniques for static and/ordynamic point clouds.

-   Non-Patent Literature 1: Use Cases for Point Cloud Compression,    ISO/IEC JTC1/SC29 WG11 Doc. N16331, Geneva, CH, June 2016.-   Non-Patent Literature 2: Requirements for Point Cloud Compression,    ISO/IEC JTC1/SC29 WG11 Doc. N16330, Geneva, CH, June 2016.

SUMMARY

The present disclosure is directed toward Point Cloud Coding (PCC). Asdescribed herein, flexible tree partitioning representation and codingare elaborated on, e.g., for the purpose of point cloud compression.

According to an aspect of the disclosure, a method of decoding encodedinformation of a point cloud may be performed by at least one processorand may comprise: obtaining an encoded bitstream, the encoded bitstreamincluding encoded information of a point cloud including a set of pointsin a three-dimensional space; and determining a type of partitioningused to encode the information of the point cloud by at least one ofparsing signals of at least three binary syntaxes or inferring at leastone syntax of the at least three binary syntaxes.

According to a further aspect of the disclosure, the at least threebinary syntaxes may indicate whether partitioning is applied on X, Y,and Z axes.

According to a further aspect of the disclosure, the method may furthercomprise: obtaining a node size of an object of the partitioned pointcloud, wherein the determining the type of partitioning may be performedby inferring the at least three binary syntaxes based on the node sizeof the object.

According to a further aspect of the disclosure, the determining thetype of partitioning may be performed by at least one of the followingfurther operations: parsing signals of two additional binary syntaxesand inferring the additional binary syntaxes.

According to a further aspect of the disclosure, the type ofpartitioning of the point cloud may be selected using the followingformula: p{circumflex over ( )}=arg min

p

{D(p)+λ·R(p)}, wherein where p indicates a tree partition mode, D(p) isa distortion function, R(p) is a rate function, λ is a Laplaciancoefficient, and p{circumflex over ( )} is a tree partition mode.

According to a further aspect of the disclosure, the determining of thetype of partitioning may be performed by parsing one of the at leastthree binary syntaxes and inferring two of the at least three binarysyntaxes.

According to a further aspect of the disclosure, the determining thetype of partitioning may include inferring a partitioning type of adescendant node of the partitioned point cloud.

According to a further of the disclosure, an apparatus for codinginformation of a point cloud may comprise: at least one memoryconfigured to store program code; and at least one processor configuredto access the at least one memory and operate according to the computerprogram code; the computer program code may comprise: first obtainingcode configured to obtain an encoded bitstream, the encoded bitstreamincluding encoded information of a point cloud including a set of pointsin a three-dimensional space; and determining code configured todetermine a type of partitioning used to encode the information of thepoint cloud by at least one of parsing signals of at least three binarysyntaxes or inferring at least one syntax of the at least three binarysyntaxes.

According to a further aspect of the disclosure the at least threebinary syntaxes may indicate whether partitioning is applied on X, Y,and Z axes.

According to a further aspect of the disclosure the computer programcode may further comprise: second obtaining code configured to obtain anode size of an object of the partitioned point cloud, wherein thedetermining code is further configured to determine the type ofpartitioning by inferring the at least three binary syntaxes based onthe node size of the object.

According to a further aspect of the disclosure, the determining codemay be further configured to determine the type of partitioning by atleast one of the following operations: parsing signals of two additionalbinary syntaxes and inferring the additional binary syntaxes.

According to a further aspect of the disclosure, the type ofpartitioning of the point cloud may be selected using the followingformula: p{circumflex over ( )}=arg min

p

{D(p)+λ·R(p)}, wherein where p indicates a tree partition mode, D(p) isa distortion function, R(p) is a rate function, λ is a Laplaciancoefficient, and p{circumflex over ( )} is a tree partition mode.

According to a further aspect of the disclosure, the determining codemay be configured to determine the type of partitioning by parsing oneof the at least three binary syntaxes and inferring two of the at leastthree binary syntaxes.

According to an aspect of the disclosure a non-transitorycomputer-readable storage medium may instructions that cause at leastone processor to: obtain an encoded bitstream, the encoded bitstreamincluding encoded information of a point cloud including a set of pointsin a three-dimensional space; and determine a type of partitioning usedto encode the information of the point cloud by at least one of parsingsignals of at least three binary syntaxes or inferring at least onesyntax of the at least three binary syntaxes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an octree partition in three-dimensionalspace.

FIG. 2 is an illustration of an octree-partition and tree structure.

FIG. 3 is an illustration of a quad-tree partition of athree-dimensional cube.

FIG. 4 is an illustration of a binary-tree partition of athree-dimensional cube.

FIG. 5 is an illustration of an asymmetric partition in two-dimensionalspace.

FIG. 6 is an illustration of a two-level hybrid partition intwo-dimensional space.

FIG. 7 is a flowchart of an example process for decoding encodedinformation of a point cloud

FIG. 8 is a diagram of an example environment in which systems and/ormethods, described herein, may be implemented.

FIG. 9 is a diagram of example components of one or more devices of FIG.8 .

DETAILED DESCRIPTION Point-Cloud Compression in Test Model 13 (TMC13) inMPEG

As described in Non-Patent Literature 3 and Non-Patent Literature 4, inthe Test Model 13 (TMC13) codec, promulgated by the MPEG, geometryinformation and associated attributes, such as color or reflectance, ofpoints of a point cloud, are separately compressed. The geometryinformation, e.g., 3D coordinates of the points, is coded byoctree-partitioning with occupancy information. The attributes are thencompressed based on reconstructed geometry using prediction, lifting andregion adaptive hierarchical transform techniques. Theoctree-partitioning and occupancy encoding processes are described inmore detail below.

-   Non-Patent Literature 3: G-PCC Codec description, ISO/IEC    JTC1/SC29/WG11, Doc. N18891, October 2019.-   Non-Patent Literature 4: Text of ISO/IEC CD 23090-9 Geometry-based    Point Cloud Compression, ISO/IEC JTC1/SC29 WG11 Doc. N18478, Geneva,    July 2019.

Octree Partitioning

In TMC13, if an octree geometry codec is used, the geometry encodingproceeds as follows: First, a cubical axis-aligned bounding box B isdefined by two points (0,0,0) and (2^(M-1), 2^(M-1), 2^(M-1)) where2^(M-1) defines the size of B and M is specified in the bitstream. Anoctree structure is then built by recursively subdividing B. At eachstage, a cube is subdivided into 8 sub-cubes. An 8-bit code, namely theoccupancy code, is then generated by associating a 1-bit value with eachsub-cube in order to indicate whether it contains points (i.e., whetherit is full and has a value of 1) or not (i.e., whether it is empty andhas a value of 0). Only full sub-cubes with a size greater than 1 (i.e.,non-voxels) are further subdivided. FIG. 1 presents an illustration ofan octree-partition in 3D space.

An example of two-level octree partition and the corresponding occupancycode are shown in FIG. 2 , where cubes and nodes in dark indicate thatthey are occupied by points.

Encoding of Occupancy Code

The occupancy code of each node is then compressed by an arithmeticencoder. The occupancy code can be denoted as S, which is an 8-bitinteger, and each bit in S indicates the occupancy status of each childnode. Two encoding methods for occupancy code exist in TMC13: bit-wiseencoding and byte-wise encoding. Bit-wise encoding is enabled bydefault. Both methods perform arithmetic coding with context modeling toencode the occupancy code, and the context status is initialized at thebeginning of the coding process and is updated during the codingprocess.

For bit-wise encoding, eight bins in S are encoded in a certain orderwhere each bin is encoded by referring to the occupancy status ofneighboring nodes and child nodes of neighboring nodes, where theneighboring nodes are in the same level of a current node.

For byte-wise encoding, S is encoded by referring to:

-   -   an adaptive look up table (A-LUT), which keeps track of the N        (e.g., 32) most frequent occupancy codes, and    -   a cache which keeps track of the last different observed M        (e.g., 16) occupancy codes.

A binary flag indicating whether S is the A-LUT or not is encoded. If Sis in the A-LUT, the index in the A-LUT is encoded by using a binaryarithmetic encoder. If S is not in the A-LUT, then a binary flagindicating whether S is in the cache or not is encoded. If S is in thecache, then the binary representation of its index is encoded by using abinary arithmetic encoder. Otherwise, if S is not in the cache, then thebinary representation of S is encoded by using a binary arithmeticencoder.

The decoding process starts by parsing dimensions of the bounding box Bfrom the bitstream. The same octree structure is then built bysubdividing B according to the decoded occupancy codes.

Still, these methods and techniques have room for improvement, primarilyin terms of flexible tree partitioning representation, which isdiscussed below (for purposes of point cloud compression).

The proposed methods and apparatuses may be used separately or combinedin any order. Further, each of the methods (or embodiments), encoder,and decoder may be implemented by processing circuitry (e.g., one ormore processors or one or more integrated circuits). In one example, theone or more processors execute a program that is stored in anon-transitory computer-readable medium. Further, the disclosures hereinare not limited to uses related to TMC13 software or the MPEG-PCCstandard.

Flexible Tree Partition

According to embodiments described herein, methods and techniques oftree partitioning are disclosed.

According to embodiments and disclosed techniques, a node (e.g., a cubein 3D space) in a tree structure can be split in different forms,including but not limited to the following: quad-tree partitioning,binary-tree partitioning, symmetric partitioning, asymmetricpartitioning, hybrid partitioning, and flexible partitioning.

Symmetric Partitions

Symmetric oct-tree (OT) partitioning is described as follows. As shownin FIG. 1 , a cube in 3D space can be divided into 8 sub-cubes (havingthe same size) by 3 planes perpendicular to x, y, and z axes,respectively. That is, partition may be applied along x, y, z axes.

Symmetric quad-tree (QT) partitioning is described as follows. As shownin FIG. 3 , a cube in 3D space can be divided into 4 sub-cubes (havingthe same size) by 2 perpendicular planes. That is, partitioning may beapplied along two out of three axes.

Symmetric binary-tree (BT) partitioning is described as follows. Asshown in FIG. 4 , a cube in 3D can be divided into 2 sub-cubes (havingthe same size) by 1 plane perpendicular to one of the x, y, and z axis.That is, partitioning may be applied along one axis only.

Asymmetric Partitioning

According to embodiments, asymmetric partitioning can be applied whenthe partition position is not in the middle (e.g., other than theabovementioned symmetric partitions. 2D illustrations of asymmetricpartitions are shown in FIG. 5 . 3D asymmetric partitions may includethe following:

-   -   Asymmetric OT partition. A cube in 3D space can be divided into        8 sub-cubes (having different sizes) by 3 planes perpendicular        to x, y, and z axis respectively. The partition position along        each axis may or may not be in the middle.    -   Asymmetric QT partition. A cube in 3D space can be divided into        4 sub-cubes (having different sizes) by 2 perpendicular planes.        The partition position along each axis may or may not be in the        middle.    -   Asymmetric BT partition. A cube in 3D space can be divided into        2 sub-cubes (having different sizes) by 1 plane perpendicular to        one of x, y and z axis. The partition position may or may not be        in the middle.

According to embodiments, typical partition ratios in asymmetric modesare 1:3 and 3:1, however, in other cases the partition ratio could bearbitrary, so long as the partitions are on integer positions.Constraints may be involved in asymmetric partitions, including but notlimited to the following:

-   -   Asymmetric partitions may not be allowed only when the node size        is within a specified range. For example, in some embodiments, a        2×2×2 cube or even a cube larger than 8×8×8 may not be        applicable for asymmetric partitioning.    -   Asymmetric partitions may not be allowed when the node is a        cube, e.g., a cube that can only be partitioned by a symmetric        OT/QT/BT partition.

Hybrid Partitioning

According to embodiments, a node may be partitioned as if it has beendivided multiple times at once, e.g., a node may be portioned via ahybrid partitioning scheme. A 2D illustration of a two-level hybridpartition scheme is shown in FIG. 6 , where the two-levels indicate twosequential partitions. The left-hand side of FIG. 6 shows a case wherean asymmetric BT partition (3:1) is applied along the x-axis and asymmetric BT partition (1:1) is then applied to the sub-node in a lowerpart along the y-axis. The right-hand side of FIG. 6 shows a case wherea symmetric BT partition (1:1) is applied along the x-axis and twosymmetric BT partitions (1:1) are then applied to the two sub-nodesalong the x-axis again. The left-hand side of FIG. 6 can be interpretedas a triple-tree partition because it results in three sub-nodes, andthe right-hand side of FIG. 6 can be interpreted as another form ofquad-tree partitioning.

In 3D space, the combination of hybrid partitioning can be complicated.Therefore, simplifications and constraints may be involved, includingbut not limited to the following,

-   -   Hybrid partitions higher than two-levels may not be allowed,        this may indicate that a node can be partitioned two times,        maximum, at once.    -   Limited combinations of partitioning may be allowed in hybrid        partitions. One can predefine a group of hybrid partition modes        that can be used.    -   Hybrid partitions may be allowed only when the node size is        within a specified range. For example, a 2×2×2 cube or a cube        larger than 8×8×8 may be not applicable for hybrid partitioning.    -   Hybrid partitions may be allowed only when the node is a cube,        i.e., a rectangular cuboid may not be applicable for hybrid        partitioning.    -   If a hybrid partition is equivalent to a non-hybrid partition,        then this hybrid partition may be invalid. For example, if a        symmetric OT partition is equivalent to a hybrid partition (a        symmetric BT plus two symmetric QTs), then this type of hybrid        partition may be invalid.

Representation of Flexible Tree Partitioning

Disclosed herein are techniques for flexible tree partitioning thatprovide for additivity, wherein a tree node can be flexibly partitioned.A preferred partition mode may be determined on an encoder-side by arate-distortion optimization technique, where the preferred modeminimizes the cost function as below:

${\hat{p} = {\arg\mspace{11mu}{\min\limits_{p}\left\{ {{D(p)} + {\lambda \cdot {R(p)}}} \right\}}}},$Here, p indicates a tree partition mode, D(p) and R(p) are distortionand rate functions in terms of p, λ is the Laplacian coefficient thatbalances between rate and distortion term, {circumflex over (p)} is thepreferred tree partition mode that minimizes the cost function. Notethat the distortion function D(p) may include distortions of attributesif the attribute and geometry are encoded jointly. If the geometrycoding is lossless, the distortion function can be eliminated becauseD(p) is always 0 in this case. The preferred partition mode p may thensignaled by syntax elements in the bitstream.

According to embodiments, in order to efficiently encode the flexibletree structures, the splitting and occupancy information can berepresented in binary forms. Note that the below disclosed syntaxes canbe coded by bypassing or by entropy coding with context modeling.

Symmetric Partitioning

According to embodiments, in order to represent symmetric OT, QT and BTpartitions, three binary syntax split_x, split_y and split_z may besignaled, indicating whether the partition is applied to the x, y, and zaxis, respectively. The values determine the partition mode as follows:

TABLE 1 Symmetric Partition Signaling split_x split_y split_z Partitionmode 1 1 1 Symmetric OT 1 1 0 Symmetric QT along x-y axes 1 0 1Symmetric QT along x-z axes 0 1 1 Symmetric QT along y-z axes 1 0 0Symmetric BT along x axis 0 1 0 Symmetric BT along y axis 0 0 1Symmetric BT along z axis

According to other embodiments, the syntaxes may be inferred, ratherthan explicitly signaled.

According to an embodiment, if a node size in one dimension is below athreshold, or satisfies a predefined condition, the splitting syntaxalong this dimension can be inferred. For example, if the node size is4×4×1, the split_z can be inferred to be zero, because z dimensioncannot be further divided.

According to an embodiment, if two out of three syntax elements areknown as zeros, the last syntax element can be inferred to be equalto 1. For example, if split_x and split_y are signaled as zeros, split_zcan be inferred to be 1, because they may not allowed to be 0 at thesimultaneously.

According to an embodiment, if a node is a cube, only symmetric OT maybe allowed, therefore split_x, split_y and split_z can be inferred as 1without signaling.

According to an embodiment, the splitting syntaxes may be applied to thecurrent node, its child nodes, its grandchild nodes, and even down tothe leaf nodes. In this case, the splitting syntax elements of thedescendant nodes can be inferred without explicit signaling.

According to an embodiment, after breadth-first coding of severalpartition depths, the remaining sub-nodes can be viewed as sub-trees.Each sub-tree can be split in different ways, therefore, splittingsyntaxes may be signaled for each partition depth of each sub-tree.

According to an embodiment, the splitting syntaxes of the nodes in thesame partition depth may be assumed to be equal, in which case, only oneset of splitting syntaxes may be signaled for each partition depth.

According to an embodiment, the flexible partition modes may only beallowed when certain conditions are met, in which cases, the splittingsyntaxes are not signaled. For example, flexible partition modes may beonly allowed when the partition depth reaches a threshold. Thus,splitting syntaxes can be inferred to be 1 when the partition depth isbelow the threshold, indicating only the symmetric OT partition isallowed.

Asymmetric Partitioning

According to embodiments wherein asymmetric partitions are used,additional syntaxes, split_pos_x, split_pos_y, split_pos_z, specifyingthe partition positions may be used. The partition position may take upto 3 values, indicating whether the partition is symmetric (1:1) orasymmetric (1:3 or 3:1). Taking split_pos_x for example, if split_x isequal to 1, the partition ratio can be defined by two bits as follows:

TABLE 2 Asymmetric Partition Signaling split_pos_x Partition ratio b01:1 (symmetric) b10 1:3 (asymmetric) b11 3:1 (asymmetric)

According to embodiments, the first bit may indicate whether thepartition is symmetric (1:1) or not, and the second bit may indicatewhether the partition ratio is 1:3 or 3:1, and if the partition isasymmetric. If split_x is equal to 0, split_pos_x is not signaled. Thesame definitions may apply to split_pos_y and split_pos_z.

Embodiments also disclose techniques for inferring these syntaxesinstead of explicitly signaling:

According to an embodiment, if a node size in one dimension is below athreshold or satisfies a predefined condition, the splitting syntaxalong this dimension may be inferred. For example, if the asymmetricpartition is only allowed for node sizes smaller than or equal to 4,split_pos_x, split_pos_y, split_pos_z may not be signaled when the nodesize is larger than 4.

According to an embodiment, asymmetric partitions may not be allowedwhen the node is a cube, therefore in which case split_pos_x,split_pos_y, split_pos_z can be inferred as being equal to 0, withoutsignaling.

According to an embodiment, the same splitting positions may be appliedto the current node, its child nodes, and its grandchild nodes. Further,the splitting positions may even be applied to the leaf nodes, in whichcases, the splitting syntaxes of the descendant nodes can be inferredwithout explicit signaling.

According to an embodiment the splitting syntaxes of the nodes in thesame partition depth may be assumed to be equal, in which case, only oneset of splitting syntaxes may be signaled for each partition depth.

According to an embodiment, the asymmetric partition modes may only beallowed when certain conditions are met, in which cases, the splittingposition syntaxes may not be signaled when conditions are not met. Forexample, asymmetric partition modes may be only allowed when thepartition depth reaches a threshold, thus splitting position syntaxescan be inferred to be equal to 0 when the partition depth is below thethreshold, indicating that only the symmetric partitions are allowed.

Hybrid Partitioning

According to embodiments, hybrid partitioning can be signaled by a flaghybrid_split_flag and an index hybrid_split_index. If thehybrid_split_flag is equal to 1, hybrid_split_index may be signaled toindicate the hybrid partition mode referring to a predefined look-uptable. If the hybrid_split_flag is set equal to 0, hybrid_split_indexmay not be signaled, and symmetric and (or) asymmetric partition modesmay be signaled as described above. The look-up table, e.g., the mappingbetween hybrid_split_index and the hybrid partition mode may be asfollows:

TABLE 3 Hybrid Partition Signaling hybrid_split_index Hybrid partitionmode b000 Mode 0 b001 Mode 1 . . . . . . b111 Mode 7

Similar as to discussed above, according to embodiments, these syntaxesmay be inferred in addition and/or to being signaled.

According to an embodiment, hybrid partitions may only be allowed whenthe node is a cube, therefore hybrid_split_flag can be inferred as beingequal to 0 and hybrid_split_index may not be signaled when the currentnode is a non-cube.

According to an embodiment, the same splitting positions may be appliedto the current node, its child nodes, its grandchild nodes, and even theleaf nodes, in which cases, the splitting syntaxes of the descendantnodes can be inferred without explicit signaling.

According to an embodiment, the splitting syntaxes of the nodes in thesame partition depth may be assumed to be equal, in which case, only oneset of splitting syntaxes may be signaled for each partition depth.

According to an embodiment, some hybrid partition modes are not allowedwhen certain conditions are met, in which cases, the look-up table isshrinking, and bits can be saved from redundancies.

According to an embodiment, the signaling of hybrid partition modes andthe other symmetric and asymmetric modes may be unified by a singlelook-up table, which may include all possible partition modes for acurrent node. In this case, only one index referring to the look-uptable may be specified to indicate the partition mode.

Occupancy Coding in Flexible Tree Partitioning

The instant disclosure provides techniques wherein flexible treepartitioning may divide a node into less than eight sub-nodes and eachsub-node may have different sizes. For example, an asymmetric QTpartition in 3D may produce 4 sub-nodes with different sizes. Thereforeaccording to embodiments, the occupancy code may not always be in8-bits. If a node is divided into N sub-nodes, then the occupancy codeis N-bits, where each bit represents whether the corresponding sub-nodeis occupied or empty. Different node sizes may have an impact on thecontext modeling in occupancy coding.

FIG. 7 is a flow chart of an example process 700 for decoding encodedinformation of a point cloud. In some implementations, one or moreprocess blocks of FIG. 7 may be performed by user device 810. In someimplementations, one or more process blocks of FIG. 7 may be performedby another device or a group of devices separate from or including userdevice 810, such as platform 820.

As shown in FIG. 7 , process 700 may include obtaining an encodedbitstream, the encoded bitstream including encoded information of apoint cloud including a set of points in a three-dimensional space(block 710).

The point cloud may be a set of points in a 3D space, each withassociated attributes, e.g. color, material properties, etc.

As further shown in FIG. 7 , process 700 may include determining a typeof partitioning used to encode the information of the point cloud by atleast one of parsing signals of at least three binary syntaxes orinferring at least one syntax of the at least three binary syntaxes.(block 720).

FIG. 8 is a diagram of an example communication system 800 in whichsystems and/or methods, described herein, may be implemented. As shownin FIG. 8 , communication system 800 may include a user device 810, aplatform 820, and a network 830. Devices of communication system 800 mayinterconnect via wired connections, wireless connections, or acombination of wired and wireless connections.

The communication system 800 may support unidirectional transmission ofdata. For example, a first user device 810 may code video data at alocal location for transmission to a second user device 810 via thenetwork 830. The second user device 810 may receive the coded video dataof the first user device 810 from the network 830, decode the coded dataand display the recovered video data. Unidirectional data transmissionmay be common in media serving applications, and the like.

The communication system 800 may support bidirectional transmission ofdata. For example, the communication system 800 may supportbidirectional transmission of coded video that may occur, for example,during videoconferencing. For bidirectional transmission of data, eachuser device 810 may code video data captured at a local location fortransmission to the other user device 810 via the network 830. Each userdevice 810 also may receive the coded video data transmitted by theother user device 810, may decode the coded data and may display therecovered video data at a local display device.

User device 810 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information associatedwith platform 820. For example, user device 810 may include a computingdevice (e.g., a desktop computer, a laptop computer, a tablet computer,a handheld computer, a smart speaker, a server, etc.), a mobile phone(e.g., a smart phone, a radiotelephone, etc.), a wearable device (e.g.,a pair of smart glasses or a smart watch), or a similar device. In someimplementations, user device 810 may receive information from and/ortransmit information to platform 820.

Platform 820 includes one or more devices capable of providinginformation to user device 810, as described elsewhere herein. In someimplementations, platform 820 may include a cloud server or a group ofcloud servers. In some implementations, platform 820 may be designed tobe modular such that certain software components may be swapped in orout depending on a particular need. As such, platform 820 may be easilyand/or quickly reconfigured for different uses.

In some implementations, as shown, platform 820 may be hosted in cloudcomputing environment 822. Notably, while implementations describedherein describe platform 820 as being hosted in cloud computingenvironment 822, in some implementations, platform 820 is not becloud-based (i.e., may be implemented outside of a cloud computingenvironment) or may be partially cloud-based.

Cloud computing environment 822 includes an environment that hostsplatform 820. Cloud computing environment 822 may provide computation,software, data access, storage, etc. services that do not requireend-user (e.g., user device 810) knowledge of a physical location andconfiguration of system(s) and/or device(s) that hosts platform 820. Asshown, cloud computing environment 822 may include a group of computingresources 824 (referred to collectively as “computing resources 824” andindividually as “computing resource 824”).

Computing resource 824 includes one or more personal computers,workstation computers, server devices, or other types of computationand/or communication devices. In some implementations, computingresource 824 may host platform 820. The cloud resources may includecompute instances executing in computing resource 824, storage devicesprovided in computing resource 824, data transfer devices provided bycomputing resource 824, etc. In some implementations, computing resource824 may communicate with other computing resources 824 via wiredconnections, wireless connections, or a combination of wired andwireless connections.

As further shown in FIG. 8 , computing resource 824 includes a group ofcloud resources, such as one or more applications (“APPs”) 824-1, one ormore virtual machines (“VMs”) 824-2, virtualized storage (“VSs”) 824-3,one or more hypervisors (“HYPs”) 824-4, or the like.

Application 824-1 includes one or more software applications that may beprovided to or accessed by user device 810. Application 824-1 mayeliminate a need to install and execute the software applications onuser device 810. For example, application 824-1 may include softwareassociated with platform 820 and/or any other software capable of beingprovided via cloud computing environment 822. In some implementations,one application 824-1 may send/receive information to/from one or moreother applications 824-1, via virtual machine 824-2.

Virtual machine 824-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 824-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 824-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program, and may support a single process. In someimplementations, virtual machine 824-2 may execute on behalf of a user(e.g., user device 810), and may manage infrastructure of cloudcomputing environment 822, such as data management, synchronization, orlong-duration data transfers.

Virtualized storage 824-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 824. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 824-4 may provide hardware virtualization techniques thatallow multiple operating systems (e.g., “guest operating systems”) toexecute concurrently on a host computer, such as computing resource 824.Hypervisor 824-4 may present a virtual operating platform to the guestoperating systems, and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 830 includes one or more wired and/or wireless networks. Forexample, network 830 may include a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a local area network (LAN),a wide area network (WAN), a metropolitan area network (MAN), atelephone network (e.g., the

Public Switched Telephone Network (PSTN)), a private network, an ad hocnetwork, an intranet, the Internet, a fiber optic-based network, or thelike, and/or a combination of these or other types of networks.

The number and arrangement of devices and networks shown in FIG. 8 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 8 . Furthermore, two or more devices shown in FIG. 8 maybe implemented within a single device, or a single device shown in FIG.8 may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 800 may perform one or more functions described as beingperformed by another set of devices of environment 800.

FIG. 9 is a diagram of example components of a device 900. Device 900may correspond to user device 810 and/or platform 820. As shown in FIG.8 , device 900 may include a bus 910, a processor 920, a memory 930, astorage component 940, an input component 950, an output component 960,and a communication interface 970.

Bus 910 includes a component that permits communication among thecomponents of device 900. Processor 920 is implemented in hardware,firmware, or a combination of hardware and software. Processor 820 is acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 920includes one or more processors capable of being programmed to perform afunction. Memory 930 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 920.

Storage component 940 stores information and/or software related to theoperation and use of device 900. For example, storage component 940 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, and/or a solid state disk), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 950 includes a component that permits device 900 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 950 mayinclude a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, and/or anactuator). Output component 960 includes a component that providesoutput information from device 900 (e.g., a display, a speaker, and/orone or more light-emitting diodes (LEDs)).

Communication interface 970 includes a transceiver-like component (e.g.,a transceiver and/or a separate receiver and transmitter) that enablesdevice 900 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 970 may permit device 900to receive information from another device and/or provide information toanother device. For example, communication interface 970 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface, orthe like.

Device 900 may perform one or more processes described herein. Device900 may perform these processes in response to processor 920 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 930 and/or storage component 940. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 930 and/or storagecomponent 940 from another computer-readable medium or from anotherdevice via communication interface 970. When executed, softwareinstructions stored in memory 930 and/or storage component 940 may causeprocessor 920 to perform one or more processes described herein.

Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 9 are provided asan example. In practice, device 900 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 9 . Additionally, or alternatively,a set of components (e.g., one or more components) of device 900 mayperform one or more functions described as being performed by anotherset of components of device 900.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations are possible inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term component is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of possible implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A method of compressing information of a pointcloud, the method performed by at least one processor and comprising:obtaining data of a point cloud including a set of points in athree-dimensional space; partitioning the data using flexible treepartitioning and applying at least one of symmetric partitioning,asymmetric partitioning, and hybrid partitioning; and encoding theflexible tree partitioning by representing splitting and occupancyinformation thereof in binary form.
 2. The method of claim 1, whereinthe encoding the flexible tree partitioning includes representing thesplitting and occupancy information using three binary syntaxes.
 3. Themethod of claim 1, wherein a type of partitioning applied using theflexible tree partitioning depends upon a node size.
 4. The method ofclaim 1, wherein when the partitioning of the data using the flexibletree partitioning includes applying the symmetric partitioning, and thesymmetric partitioning includes partitioning applied to a cubic node,octree symmetric partitioning is applied.
 5. The method of claim 1,wherein the partitioning of the data using the flexible treepartitioning includes applying the asymmetric partitioning when at leastone of the following conditions are met: a node size is below athreshold size or a specific partitioning depth is reached.
 6. Themethod of claim 1, wherein when the partitioning of the data using theflexible tree partitioning includes applying the hybrid partitioning,signaling of the hybrid partitioning is unified by a single look-uptable.
 7. The method of claim 1, wherein when the partitioning of thedata using the flexible tree partitioning includes applying thesymmetric partitioning, identical splitting syntaxes are applied to acurrent node, child nodes of the current node, grandchild nodes of thecurrent node, and leaf nodes of the current node.
 8. The method of claim1, wherein the partitioning of the data using the flexible treepartitioning includes applying the asymmetric partitioning when a nodeis cubic.
 9. The method of claim 1, wherein when the partitioning of thedata using the flexible tree partitioning includes applying the hybridpartitioning, identical splitting syntaxes are applied to a currentnode, child nodes of the current node, grandchild nodes of the currentnode, and leaf nodes of the current node.
 10. The method of claim 1,wherein the partitioning of the data using the flexible treepartitioning includes applying the at least one of the symmetricpartitioning, the asymmetric partitioning, and the hybrid partitioningaccording to the following formula:$\hat{p} = {\arg\mspace{11mu}{\min\limits_{p}\left\{ {{D(p)} + {\lambda \cdot {R(p)}}} \right\}}}$wherein p indicates a tree partition mode, D(p) is a distortionfunction, R(p) is a rate function, λ is a Laplacian coefficient, and{circumflex over (p)} is a tree partition mode.
 11. An apparatus forcompressing information of a point cloud, the apparatus comprising: atleast one memory configured to store program code; and at least oneprocessor configured to access the at least one memory and operateaccording to the program code, the program code comprising: obtainingcode configured to obtain data of a point cloud including a set ofpoints in a three-dimensional space; partitioning code configured topartition the data using flexible tree partitioning and apply at leastone of symmetric partitioning, asymmetric partitioning, and hybridpartitioning; and encoding code configured to encode the flexible treepartitioning by representing splitting and occupancy information thereofin binary form.
 12. The apparatus of claim 11, wherein the encoding codeis configured to encode the flexible tree partitioning by representingthe splitting and occupancy information using three binary syntaxes. 13.The apparatus of claim 11, wherein the partitioning code is configuredto partition the data using the flexible tree partitioning and apply theat least one of the symmetric partitioning, the asymmetric partitioning,and the hybrid partitioning depending upon a node size.
 14. Theapparatus of claim 11, wherein the partitioning code is configured topartition the data using the flexible tree partitioning by applying thesymmetric partitioning such that octree symmetric partitioning isapplied to a cubic node.
 15. The apparatus of claim 11, wherein thepartitioning code is configured to partition the data using the flexibletree partitioning by applying the asymmetric partitioning when at leastone of the following conditions are met: a node size is below athreshold size or a specific partitioning depth is reached.
 16. Theapparatus of claim 11, wherein the partitioning code is configured topartition the data using the flexible tree partitioning by applying thehybrid partitioning such that signaling of the hybrid partitioning isunified by a single look-up table.
 17. The apparatus of claim 11,wherein the partitioning code is configured to partition the data usingthe flexible tree partitioning by applying the symmetric partitioningsuch that identical splitting syntaxes are applied to a current node,child nodes of the current node, grandchild nodes of the current node,and leaf nodes of the current node.
 18. The apparatus of claim 11,wherein the partitioning code is configured to partition the data usingthe flexible tree partitioning by applying the asymmetric partitioningwhen a node is cubic.
 19. The apparatus of claim 11, wherein thepartitioning code is configured to partition the data using the flexibletree partitioning by applying the at least one of the symmetricpartitioning, the asymmetric partitioning, and the hybrid partitioningaccording to the following formula:$\hat{p} = {\arg\mspace{11mu}{\min\limits_{p}\left\{ {{D(p)} + {\lambda \cdot {R(p)}}} \right\}}}$wherein p indicates a tree partition mode, D(p) is a distortionfunction, R(p) is a rate function, λ is a Laplacian coefficient, and{circumflex over (p)} is a tree partition mode.
 20. A non-transitorycomputer-readable storage medium storing instructions that cause atleast one processor to: obtain data of a point cloud including a set ofpoints in a three-dimensional space; partition the data using flexibletree partitioning and applying at least one of symmetric partitioning,asymmetric partitioning, and hybrid partitioning; and encode theflexible tree partitioning by representing splitting and occupancyinformation thereof in binary form.